Welcome to PRR-HyPred


PRR-HyPred is a two-layer ML-based hybrid easy to use webserver for the prediction of pattern recognition receptors (PRRs). PRR-HyPred was developed by using different feature sets that includes, amino acid composition, dipeptide composition, physiochemical properties, and their hybrids as an input feature. The PRR-HyPred working module is based on optimally selected hybrid features in which the first layer predicts whether a given sequence is a PRR or non-PRR, and the second layer assigs specific family to the predicted PRR sequence. In addition to the prediction results, the probability scores for each prediction are also provided.


 Availability
Standalone Webserver

Reference:
PRR-HyPred: A machine learning-based two-layer hybrid framework to predict pattern-recognition receptors and their families by employing sequence encoded optimal features (Manuscript Submitted): DOI:xxxx
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